Language understanding applications (e.g., digital assistant applications) require at least some contextual language understanding for interpreting spoken language input. The key to success for language understanding applications is having data. However, at early stages of language understanding system development, data is usually limited, in particular, for multi-turn dialogue scenarios. Due to the lack of data, current commercial language understanding systems have limited support for multi-turn scenarios and tend to rely on deterministic rules. Consequently, as commonly known to the community, rule-based systems do not provide accurate and reliable information in multi-turn scenarios.
It is with respect to these and other general considerations that embodiments have been made. Also, although relatively specific problems have been discussed, it should be understood that the embodiments should not be limited to solving the specific problems identified in the background.